"""ShuffleNet in PyTorch.
See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
"""
import math

import torch
import torch.nn as nn
import torch.nn.functional as F

__all__ = ["ShuffleV1_aux", "ShuffleV1"]


class ShuffleBlock(nn.Module):
    def __init__(self, groups):
        super(ShuffleBlock, self).__init__()
        self.groups = groups

    def forward(self, x):
        """Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
        N, C, H, W = x.size()
        g = self.groups
        return x.reshape(N, g, C // g, H, W).permute(0, 2, 1, 3, 4).reshape(N, C, H, W)


class Bottleneck(nn.Module):
    def __init__(self, in_planes, out_planes, stride, groups, is_last=False):
        super(Bottleneck, self).__init__()
        self.is_last = is_last
        self.stride = stride

        mid_planes = int(out_planes / 4)
        g = 1 if in_planes == 24 else groups
        self.conv1 = nn.Conv2d(in_planes, mid_planes, kernel_size=1, groups=g, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_planes)
        self.shuffle1 = ShuffleBlock(groups=g)
        self.conv2 = nn.Conv2d(
            mid_planes,
            mid_planes,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=mid_planes,
            bias=False,
        )
        self.bn2 = nn.BatchNorm2d(mid_planes)
        self.conv3 = nn.Conv2d(mid_planes, out_planes, kernel_size=1, groups=groups, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes)

        self.shortcut = nn.Sequential()
        if stride == 2:
            self.shortcut = nn.Sequential(nn.AvgPool2d(3, stride=2, padding=1))

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.shuffle1(out)
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        res = self.shortcut(x)
        preact = torch.cat([out, res], 1) if self.stride == 2 else out + res
        out = F.relu(preact)
        # out = F.relu(torch.cat([out, res], 1)) if self.stride == 2 else F.relu(out+res)
        if self.is_last:
            return out, preact
        else:
            return out


class ShuffleNet(nn.Module):
    def __init__(self, cfg, num_classes=10):
        super(ShuffleNet, self).__init__()
        out_planes = cfg["out_planes"]
        num_blocks = cfg["num_blocks"]
        groups = cfg["groups"]

        self.conv1 = nn.Conv2d(3, 24, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(24)
        self.in_planes = 24
        self.layer1 = self._make_layer(out_planes[0], num_blocks[0], groups)
        self.layer2 = self._make_layer(out_planes[1], num_blocks[1], groups)
        self.layer3 = self._make_layer(out_planes[2], num_blocks[2], groups)
        self.linear = nn.Linear(out_planes[2], num_classes)
        self.last_channel = out_planes[2]

    def _make_layer(self, out_planes, num_blocks, groups):
        layers = []
        for i in range(num_blocks):
            stride = 2 if i == 0 else 1
            cat_planes = self.in_planes if i == 0 else 0
            layers.append(
                Bottleneck(
                    self.in_planes,
                    out_planes - cat_planes,
                    stride=stride,
                    groups=groups,
                    is_last=(i == num_blocks - 1),
                )
            )
            self.in_planes = out_planes
        return nn.Sequential(*layers)

    def get_feat_modules(self):
        feat_m = nn.ModuleList([])
        feat_m.append(self.conv1)
        feat_m.append(self.bn1)
        feat_m.append(self.layer1)
        feat_m.append(self.layer2)
        feat_m.append(self.layer3)
        return feat_m

    def get_bn_before_relu(self):
        raise NotImplementedError('ShuffleNet currently is not supported for "Overhaul" teacher')

    def forward(self, x, is_feat=False):
        out = F.relu(self.bn1(self.conv1(x)))
        f0 = out
        out, f1_pre = self.layer1(out)
        f1 = out
        out, f2_pre = self.layer2(out)
        f2 = out
        out, f3_pre = self.layer3(out)
        f3 = out
        out = F.avg_pool2d(out, 4)
        out = out.reshape(out.size(0), -1)
        out = self.linear(out)
        if is_feat:
            return [f0, f1, f2, f3], out
        else:
            return out


class Auxiliary_Classifier(nn.Module):
    def __init__(self, cfg, num_classes=10):
        super(Auxiliary_Classifier, self).__init__()
        out_planes = cfg["out_planes"]
        num_blocks = cfg["num_blocks"]
        groups = cfg["groups"]

        self.in_planes = out_planes[0]
        self.block_extractor1 = nn.Sequential(
            *[
                self._make_layer(out_planes[1], num_blocks[1], groups),
                self._make_layer(out_planes[2], num_blocks[2], groups),
            ]
        )
        self.in_planes = out_planes[1]
        self.block_extractor2 = nn.Sequential(
            *[self._make_layer(out_planes[2], num_blocks[2], groups)]
        )

        self.inplanes = out_planes[2]
        self.block_extractor3 = nn.Sequential(
            *[self._make_layer(out_planes[2], num_blocks[2], groups, downsample=False)]
        )

        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc1 = nn.Linear(out_planes[2], num_classes)
        self.fc2 = nn.Linear(out_planes[2], num_classes)
        self.fc3 = nn.Linear(out_planes[2], num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def _make_layer(self, out_planes, num_blocks, groups, downsample=True):
        layers = []
        for i in range(num_blocks):
            stride = 2 if i == 0 and downsample is True else 1
            cat_planes = self.in_planes if i == 0 and downsample is True else 0
            layers.append(
                Bottleneck(
                    self.in_planes,
                    out_planes - cat_planes,
                    stride=stride,
                    groups=groups,
                    is_last=(i == num_blocks - 1),
                )
            )
            self.in_planes = out_planes
        return nn.Sequential(*layers)

    def forward(self, x):
        ss_logits = []
        for i in range(len(x)):
            idx = i + 1
            out = getattr(self, "block_extractor" + str(idx))(x[i])
            out = self.avg_pool(out)
            out = out.view(out.size(0), -1)
            out = getattr(self, "fc" + str(idx))(out)
            ss_logits.append(out)
        return ss_logits


class ShuffleNet_Auxiliary(nn.Module):
    def __init__(self, cfg, num_classes=100):
        super(ShuffleNet_Auxiliary, self).__init__()
        self.backbone = ShuffleNet(cfg, num_classes=num_classes)
        self.auxiliary_classifier = Auxiliary_Classifier(cfg, num_classes=num_classes * 4)

    def forward(self, x, grad=False):
        feats, logit = self.backbone(x, is_feat=True)
        if grad is False:
            for i in range(len(feats)):
                feats[i] = feats[i].detach()
        ss_logits = self.auxiliary_classifier(feats)
        return logit, ss_logits


def ShuffleV1(**kwargs):
    cfg = {"out_planes": [240, 480, 960], "num_blocks": [4, 8, 4], "groups": 3}
    return ShuffleNet(cfg, **kwargs)


def ShuffleV1_aux(**kwargs):
    cfg = {"out_planes": [240, 480, 960], "num_blocks": [4, 8, 4], "groups": 3}
    return ShuffleNet_Auxiliary(cfg, **kwargs)
